"""
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================

The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:

  http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

  .. _LFW: http://vis-www.cs.umass.edu/lfw/

  original source: http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html

"""



print(__doc__)

from time import time
import logging
import pylab as pl
import numpy as np

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC

def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
    """Helper function to plot a gallery of portraits"""
    pl.figure(figsize=(1.8 * n_col, 2.4 * n_row))
    pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
    for i in range(n_row * n_col):
        pl.subplot(n_row, n_col, i + 1)
        pl.imshow(images[i].reshape((h, w)), cmap=pl.cm.gray)
        pl.title(titles[i], size=12)
        pl.xticks(())
        pl.yticks(())


# plot the result of the prediction on a portion of the test set

def title(y_pred, y_test, target_names, i):
    pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
    true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
    return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)

###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction

def run(X_train, X_test, y_train, y_test, n_components=150):
  print("Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]))
  t0 = time()
  pca = PCA(n_components=n_components, whiten=True).fit(X_train)
  print("done in %0.3fs" % (time() - t0))

  eigenfaces = pca.components_.reshape((n_components, h, w))

  print("pca variance explaination: ")
  first_pca_variance = pca.explained_variance_ratio_[0]
  second_pca_variance =pca.explained_variance_ratio_[1]
  print(first_pca_variance)
  print(second_pca_variance)

  print("Projecting the input data on the eigenfaces orthonormal basis")
  t0 = time()
  X_train_pca = pca.transform(X_train)
  X_test_pca = pca.transform(X_test)
  print("done in %0.3fs" % (time() - t0))


  ###############################################################################
  # Train a SVM classification model(
  print("Fitting the classifier to the training set")
  t0 = time()
  param_grid = {
            'C': [1e3, 5e3, 1e4, 5e4, 1e5],
            'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
            }
  # for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'
  clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
  clf = clf.fit(X_train_pca, y_train)
  print("done in %0.3fs" % (time() - t0))
  print("Best estimator found by grid search:")
  print(clf.best_estimator_)


  ###############################################################################
  # Quantitative evaluation of the model quality on the test set

  print("Predicting the people names on the testing set")
  t0 = time()
  y_pred = clf.predict(X_test_pca)
  print("done in %0.3fs" % (time() - t0))

  print(classification_report(y_test, y_pred, target_names=target_names))

  print("confusion matrix: ")
  print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))


  ###############################################################################
  # Qualitative evaluation of the predictions using matplotlib

  visualize = False
  if visualize:
    prediction_titles = [title(
        y_pred, y_test, target_names, i) \
      for i in range(y_pred.shape[0])
    ]
    plot_gallery(X_test, prediction_titles, h, w)
    # plot the gallery of the most significative eigenfaces
    eigenface_titles = ["eigenface %d" % i \
      for i in range(eigenfaces.shape[0])
    ]
    plot_gallery(eigenfaces, eigenface_titles, h, w)
    pl.show()

if __name__ == "__main__":
  # Display progress logs on stdout
  logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')


  ###############################################################################
  # Download the data, if not already on disk and load it as numpy arrays
  lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

  # introspect the images arrays to find the shapes (for plotting)
  n_samples, h, w = lfw_people.images.shape
  np.random.seed(42)

  # for machine learning we use the data directly (as relative pixel
  # position info is ignored by this model)
  X = lfw_people.data
  n_features = X.shape[1]

  # the label to predict is the id of the person
  y = lfw_people.target
  target_names = lfw_people.target_names
  n_classes = target_names.shape[0]

  print("Total dataset size:")
  print("n_samples: %d" % n_samples)
  print("n_features: %d" % n_features)
  print("n_classes: %d" % n_classes)

  ###############################################################################
  # Split into a training and testing set
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
  
  # [10, 15, 25,
  # 10 takes forever
  n_components = [50, 100, 250]
  try:
    for nc in n_components:
      run(X_train, X_test, y_train, y_test, nc)
  except KeyboardInterrupt: 
    pass

